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Proceedings Paper

Automatic geometric rectification for patient registration in image-guided spinal surgery
Author(s): Yunliang Cai; Jonathan D. Olson; Xiaoyao Fan; Linton T. Evans; Keith D. Paulsen; David W. Roberts; Sohail K. Mirza; S. Scott Lollis; Songbai Ji
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Paper Abstract

Accurate and efficient patient registration is crucial for the success of image-guidance in open spinal surgery. Recently, we have established the feasibility of using intraoperative stereovision (iSV) to perform patient registration with respect to preoperative CT (pCT) in human subjects undergoing spinal surgery. Although a desired accuracy was achieved, the method required manual segmentation and placement of feature points on reconstructed iSV and pCT surfaces. In this study, we present an improved registration pipeline to eliminate these manual operations. Specifically, automatic geometric rectification was performed on spines extracted from pCT and iSV into pose-invariant shapes using a nonlinear principal component analysis (NLPCA). Rectified spines were obtained by projecting the reconstructed 3D surfaces into an anatomically determined orientation. Two-dimensional projection images were then created with image intensity values encoding feature "height" in the dorsal-ventral direction. Registration between the 2D depth maps yielded an initial point-wise correspondence between the 3D surfaces. A refined registration was achieved using an iterative closest point (ICP) algorithm. The technique was successfully applied to two explanted and one live porcine spines. The computational cost of the registration pipeline was less than 1 min, with an average target registration error (TRE) less than 2.2 mm in the laminae area. These results suggest the potential for the pose-invariant, rectification-based registration technique for clinical application in human subjects in the future.

Paper Details

Date Published: 18 March 2016
PDF: 10 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860E (18 March 2016); doi: 10.1117/12.2216193
Show Author Affiliations
Yunliang Cai, Thayer School of Engineering, Dartmouth College (United States)
Jonathan D. Olson, Thayer School of Engineering, Dartmouth College (United States)
Xiaoyao Fan, Thayer School of Engineering, Dartmouth College (United States)
Linton T. Evans, Dartmouth Hitchcock Medical Ctr. (United States)
Keith D. Paulsen, Thayer School of Engineering, Dartmouth College (United States)
Dartmouth Hitchcock Medical Ctr. (United States)
David W. Roberts, Dartmouth Hitchcock Medical Ctr. (United States)
Sohail K. Mirza, Dartmouth Hitchcock Medical Ctr. (United States)
S. Scott Lollis, Dartmouth Hitchcock Medical Ctr. (United States)
Songbai Ji, Thayer School of Engineering, Dartmouth College (United States)
Dartmouth Hitchcock Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Ziv R. Yaniv, Editor(s)

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